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dc.contributor.authorLaurence, Luke
dc.date.accessioned2024-06-07T18:13:21Z
dc.date.available2024-06-07T18:13:21Z
dc.date.issued2024-06-07
dc.identifier.urihttp://hdl.handle.net/10222/84278
dc.description.abstractDecision support tools (DSTs) and a framework for incorporating estimates of growing season nitrogen (N) mineralization (GSN) in soil, using analysis of total soil N (TN) to quantify the stable N pool, and biological nitrogen availability (BNA) to quantify the labile N pool in a zero- plus first-order kinetic function, were developed to inform N fertilizer recommendations. Overall, DSTs for biological processes as related to soil N dynamics were achieved using pedotransfer functions (PTF), digital soil mapping (DSM) and machine learning. From these results, a tiered approach to N fertilizer management was proposed. The DSM, as tier 1, provides estimates of GSN to producers who do not have point data; the PTF, as tier 2, is applied for producers who have surrogate data; and subsequent tiers are for producers with direct measures for N response variables.en_US
dc.language.isoenen_US
dc.subjectsoil nitrogen mineralizationen_US
dc.subjectpedotransfer functionsen_US
dc.subjectdigital soil mappingen_US
dc.subjectmachine learningen_US
dc.titleDeveloping Pedotransfer Functions and Spatial Estimates of Growing Season Nitrogen Mineralization to Inform Improved Nitrogen Management Strategies in Prince Edward Islanden_US
dc.date.defence2024-05-30
dc.contributor.departmentDepartment of Plant, Food and Environmental Sciencesen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.external-examinerDavid Pelsteren_US
dc.contributor.thesis-readerBrandon Heungen_US
dc.contributor.thesis-readerJudith Nyiranezaen_US
dc.contributor.thesis-supervisorDavid Burtonen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsYesen_US
dc.contributor.copyright-releaseNot Applicableen_US
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